Generating a Digital Twin, Method, System, Computer Program Product

2. The computed implemented method of claim 1, wherein the digital twin comprises at least one input interface and at least one output interface, wherein the digital twin comprises at least one functional module generating output from input.

3. The computed implemented method of claim 1, wherein the digital twin features are part of at least one of the digital win feature-groups including: calculation properties, scope of simulation, model architecture, communication safety, accessibility, data storage, encryption functions, resource allocation, or hardware requirements.

4. The computed implemented method of claim 3, wherein the digital twin features are at least one of: calculation speed, required computer performance, accuracy of calculation, hardware operating range of the digital twin, underlying physical/empirical model, complexity of physics considered /neglecting of effects, number of input parameters and/or selection of input parameters, number of output parameter and/or selection of output parameters, safety features against hacking attacks, safety features against manipulation of digital twin-tuning parameters, Dispersedness/Cloudification/Distributed system, Remote accessibility, Protected communication and/or interaction, or Gradual monitoring and/or gradual share of information.

5. The computed implemented method of claim 1, wherein the tuning module comprises an information interface for providing information to a user, the information comprising an actual setpoint of at least two tuning module parameters, the computer implemented method further comprising: providing information to a user by the information interface, the information comprising the actual setpoint of at least two tuning module parameters.

9. The system of claim 8, wherein the digital twin features are at least one of: calculation speed, required computer performance, accuracy of calculation, hardware operating range of the digital twin, underlying physical/empirical model, complexity of physics considered/neglecting of effects, number of input parameters and/or selection of input parameters, number of output parameter and/or selection of output parameters, safety features against hacking attacks, safety features against manipulation of digital twin-tuning parameters, Dispersedness/Cloudification/Distributed system, Remote accessibility, Protected communication and/or interaction, or Gradual monitoring and/or gradual share of information.

10. The system of claim 8, wherein the tuning module comprises a prediction module, wherein the tuning module comprises an information interface for providing information to a user, wherein the information interface is prepared to provide at least a tendency of change indicator of at least a second tuning module parameter when changing at least a first tuning module parameter to the user.

11. The system of claim 10, wherein the prediction module is configured to derive from the fixed tuning module parameters an estimation of at least some of the free tuning module parameter assignment.

12. The system of claim 11, wherein the prediction module comprises a neural network trained to derive from given tuning module parameters an estimation of the free tuning module parameters.